Machine Learning Engineer MMH260714-11

Momentum Services; Navi Mumbai; On-site; INR 3,000,000 - 5,500,000; Full time

Benefits offered by this job

Restaurant d'entreprise

Indemnités de stage/alternance

Job summary

Momentum Services in Navi Mumbai invites a Machine Learning Engineer to design, develop, deploy, and maintain ML models that solve complex business problems. You will work closely with data scientists, software engineers, and business stakeholders to translate data into scalable, production-ready AI systems.

The role emphasizes end-to-end ML lifecycle, from data preprocessing and feature engineering to model evaluation, monitoring, and retraining, with a focus on robust deployment, security, and

Qualifications

• 5+ years of hands-on experience in developing, training, and deploying ML models in production environments.
• Proficient in Python and ML frameworks (scikit-learn, TensorFlow, PyTorch, or Keras).
• Experience in data preprocessing, feature engineering, model evaluation, and optimization using structured and unstructured datasets.
• Exposure to cloud-based ML solutions (Azure, AWS, or GCP), MLOps practices, CI/CD pipelines, and model monitoring.

Responsibilities

• Design, build, train, validate, and deploy machine learning models to address business challenges.
• Collaborate with business stakeholders to understand requirements and translate them into AI/ML solutions.
• Perform data collection, cleansing, preprocessing, and feature engineering activities.
• Evaluate model performance using appropriate metrics and optimize models for accuracy and scalability.
• Develop and maintain data pipelines to support machine learning workflows.
• Implement MLOps best practices for model deployment, version control, monitoring, and retraining.
• Work closely with engineering teams to integrate ML solutions into production systems.
• Conduct exploratory data analysis and derive meaningful insights from large datasets.
• Prepare technical documentation, model reports, and implementation guides.
• Ensure adherence to security, governance, and compliance standards in AI implementations.
• Support production issues and continuously improve deployed machine learning solutions.
• Participate in sprint planning, code reviews, and technical discussions.
• Develop and optimize APIs and services for serving machine learning models in production environments.
• Monitor model performance, drift, and data quality, ensuring timely retraining and enhancements.
• Build scalable and reusable ML solutions that can be leveraged across multiple business use cases.
• Conduct proof of concepts (POCs) and feasibility assessments for new AI/ML initiatives.

Skills

Python programming

ML design

Data preprocessing

Stakeholder collaboration

Tools

TensorFlow

PyTorch

scikit-learn

Cloud platforms

Job description

Role Purpose

The Machine Learning Engineer is responsible for designing, developing, deploying, and maintaining machine learning models and AI-driven solutions that solve complex business problems. The role involves working closely with data scientists, software engineers, and business stakeholders to transform data into scalable and production‑ready intelligent systems.

Requirements

• 5+ years of hands‑on experience in developing, training, and deploying machine learning models in production environments.
• Strong proficiency in Python and machine learning frameworks such as scikit‑learn, TensorFlow, PyTorch, or Keras.
• Experience in data preprocessing, feature engineering, model evaluation, and optimization using structured and unstructured datasets.
• Exposure to cloud‑based ML solutions (Azure, AWS, or GCP), MLOps practices, CI/CD pipelines, and model monitoring.

Duties and Responsibilities

• Design, build, train, validate, and deploy machine learning models to address business challenges.
• Collaborate with business stakeholders to understand requirements and translate them into AI/ML solutions.
• Perform data collection, cleansing, preprocessing, and feature engineering activities.
• Evaluate model performance using appropriate metrics and optimize models for accuracy and scalability.
• Develop and maintain data pipelines to support machine learning workflows.
• Implement MLOps best practices for model deployment, version control, monitoring, and retraining.
• Work closely with engineering teams to integrate ML solutions into production systems.
• Conduct exploratory data analysis and derive meaningful insights from large datasets.
• Prepare technical documentation, model reports, and implementation guides.
• Ensure adherence to security, governance, and compliance standards in AI implementations.
• Support production issues and continuously improve deployed machine learning solutions.
• Participate in sprint planning, code reviews, and technical discussions.
• Develop and optimize APIs and services for serving machine learning models in production environments.
• Monitor model performance, drift, and data quality, ensuring timely retraining and enhancements.
• Build scalable and reusable ML solutions that can be leveraged across multiple business use cases.
• Conduct proof of concepts (POCs) and feasibility assessments for new AI/ML initiatives.